FACE-Q craniofacial module: Part 2 Psychometric properties of newly developed scales for children and young adults with facial conditions
Bibliographic record
Abstract
BACKGROUND: The FACE-Q Craniofacial Module is a patient-reported outcome measure designed for patients aged 8 to 29 years with conditions associated with a facial difference. In part 1, we describe the psychometric findings for the original CLEFT-Q scales tested in patients with cleft and noncleft facial conditions. The aim of this study was to examine psychometric performance of new FACE-Q Craniofacial Module scales. METHODS: Data were collected between December 2016 and December 2019 from patients aged 8 to 29 years with conditions associated with a visible or functional facial difference. Rasch measurement theory (RMT) analysis was used to examine psychometric properties of each scale. Scores were transformed from 0 (worst) to 100 (best) for tests of construct validity. RESULTS: 1495 participants were recruited with a broad range of conditions (e.g., birthmarks, facial paralysis, craniosynostosis, craniofacial microsomia, etc.) RMT analysis resulted in the refinement of 7 appearance scales (Birthmark, Cheeks, Chin, Eyes, Forehead, Head Shape, Smile), two function scales (Breathing, Facial), and an Appearance Distress scale. Person separation index and Cronbach alpha values met criteria. Three checklists were also formed (Eye Function, and Eye and Face Adverse Effects). Significantly lower scores on eight of nine scales were reported by participants whose appearance or functional difference was rated as a major rather than minor or no difference. Higher appearance distress correlated with lower appearance scale scores. CONCLUSION: The FACE-Q Craniofacial Module scales can be used to collect and compare patient reported outcomes data in children and young adults with a facial condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".